docs: def predict_pRF_radius(eccentricity, visual_area='V1', source='Wandell2015'):
      '''
      predict_pRF_radius(eccentricity) yields an estimate of the pRF size for a patch of cortex at the
        given eccentricity in V1.
      predict_pRF_radius(eccentricity, area) yields an estimate in the given visual area (may be given
        by the keyword visual_area).
      predict_pRF_radius(eccentricity, area, source) uses the given source to estimate the pRF size
        (may be given by the keyword source).
  
      The following visual areas can be specified:
        * 'V1' (default), 'V2', 'V3'
        * 'hV4'
        * 'V3a', 'V3b'
        * 'VO1', 'VO2'
        * 'LO1', 'LO2'
        * 'TO1', 'TO2'
  
      The following sources may be given:
        * 'Wandell2015': Wandell BA, Winawer J (2015) Computational neuroimaging and population
                         receptive fields. Trends Cogn Sci. 19(6):349-57.
                         doi:10.1016/j.tics.2015.03.009.
        * 'Kay2013: Kay KN, Winawer J, Mezer A, Wandell BA (2013) Compressive spatial summation in
                    human visual cortex. J Neurophysiol. 110(2):481-94.
      The default source is 'Wandell2015'.
      '''
      visual_area = visual_area.lower()
      if pimms.is_str(source):
          source = source.lower()
          if source not in pRF_data:
              raise ValueError('Given source (%s) not found in pRF-size database' % source)
          dat = pRF_data[source]
          dat = dat[visual_area]
      else:
          dat = {'m':source[0], 'b':source[1]}
      return dat['m']*eccentricity + dat['b']